Using a Recommendation System in an Application

This post is authored by Ankit Asthana, Principal PM VS.NET at Microsoft.

Recommendation systems are extremely popular today and are used everywhere, to predict music you’d like, products to buy, and movies to see! In this post, we would like to show you how you can build a movie recommendation engine. The post will describe how to build this model in Azure Machine Learning Studio. It comes with an end-end sample that walks you through the process of building a movie recommendation service that you can incorporate into your application today.

What’s in the sample?

The sample comes complete with code, a description of the process of building the recommendation engine, and useful tips on how to call Azure Machine Learning from a .NET application. The sample is intended for developers, and you can build the application even if you don’t have any experience with machine learning. Just follow along the steps.

What does the recommender do?

We’ve used the Matchbox Recommender provided in Azure Machine Learning to predict new movies to a user, based on their earlier ratings. The Matchbox recommender combines collaborative filtering with a content-based approach. It is therefore considered a hybrid recommender. When a user is relatively new to the system, predictions are improved by making use of the feature information about the user and items, thus addressing the well-known “cold-start” problem.

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Future samples

Have an idea for machine learning but no sure how to implement us? Drop the .NET team a line and let us know what samples you’d like to see next! This summer we’ll be rolling out multiple samples that use Cortana Intelligence Services and Azure Machine Learning,